System and method for closed loop purchase order compliance management

ABSTRACT

A system and method are provided for using an optimized order plan including freight allowance and expense per shipment. Further, an notification of non-compliance is sent to purchasing when purchasing attempts to initiate an order outside of the optimized order plan. Additionally, a compliance reporting system reports the actual shipping results as compared with the optimized order plan.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. ProvisionalApplication No. 61/435,563, filed Jan. 24, 2011, entitled “System andMethod For Transportation Management” and also claims the benefit ofU.S. Provisional Application No. 61/587,999, filed Jan. 18, 2012,entitled “System and Method For Transportation Management,” both ofwhich are hereby incorporated by reference in their entirety.

BACKGROUND OF THE INVENTION

The present invention generally relates to a system and method forlogistics. More particularly, the present invention relates to a systemand method for improving logistics cost, trailer utilization, number oftruck used, or miles driven.

Logistics involves the transportation of goods from a source to adestination. Typically, the source is a seller of goods such as amanufacturer and the destination is a buyer of goods such as a retailer.Moving goods between the source and destination at the lowest possiblecost has long been a goal of logistics and numerous prior art systemsand methods have been developed in an attempt to do so.

BRIEF SUMMARY OF THE INVENTION

One or more embodiments of the present invention provide a logisticssystem that uses an optimized order plan including freight allowance andexpense per shipment. Further, an notification of non-compliance is sentto purchasing when purchasing attempts to initiate an order outside ofthe optimized order plan. Additionally, a compliance reporting systemreports the actual shipping results as compared with the optimized orderplan.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for logistics optimization using lane orderpattern flexing according to an embodiment of the present invention.

FIG. 2 illustrates further detail of the optimization process performedby the modeling processor of FIG. 1.

FIG. 3 illustrates how the present system for logistics optimizationusing lane order pattern flexing may provide a savings increase of20-30%.

FIG. 4 illustrates the example of FIG. 3 at an inventory rather than alane level.

FIG. 5 illustrates the addition of the present system for logisticsoptimization 100 into the logistics process.

FIG. 6 illustrates a screen shot of an Inbound Transport Management(ITM) system according to an embodiment of the present invention.

FIG. 7 illustrates a screenshot of the ITM lane import criteria screen.

FIG. 8 illustrates a screenshot of the constraints preferably enteredfor the implementation of the combo model of FIG. 2.

FIG. 9 illustrates a screenshot of an ITM scenario analysis screenproviding a view of optimization solutions with the ability to lock,exclude, and mark solutions for publication.

FIG. 10 illustrates a screenshot of a lane profiles tool forvisualization and what-if analysis of lane optimization and orderflexing results.

FIG. 11 illustrates a screenshot of a lane analysis tool used to examineshipment, purchase order, inventory, and sales information summarized toa lane level to support order and route pattern determination.

FIG. 12 illustrates a screenshot of lane order profiles which includepurchasing guidelines for communication to purchasing systems orprocesses.

FIG. 13 illustrates a screenshot of the compliance detail.

FIG. 14 illustrates a screenshot of the gross margin dashboard.

FIG. 15 illustrates a business information flow according to the presentInbound Transportation Management (ITM) system.

FIG. 16 illustrates the combo model stack generation process accordingto an embodiment of the present invention.

FIG. 17 illustrates a closed loop purchase order compliance systemaccording to an embodiment of the present invention.

FIG. 18 illustrates a compliance reporting system according to apreferred embodiment of the present invention.

FIG. 19 illustrates a chart of the order compliance alerts.

FIG. 20 illustrates the order compliance screen.

FIG. 21 illustrates the PO import screen.

FIGS. 22 and 23 illustrate two examples of the order compliance alertdetail screen.

FIG. 24 illustrates the alert widget.

FIG. 25 illustrates a non-compliance report.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 illustrates a system for logistics optimization 100 using laneorder pattern flexing according to an embodiment of the presentinvention. The system for logistics optimization 100 includes an InboundTransportation Management (ITM) primary database 110, an ITM datapreparation processor 120, an ITM data importer 130, a constraint set-upprocessor 140, a scenario set-up processor 150, a modeling processor160, a model results viewing application 170, and a results publisher180.

In operation, ITM data is retrieved from the ITM database 110 by the ITMdata preparation processor 120. Preferably, the data is retrieved orrefreshed nightly, but may be retrieved or refreshed in other intervalssuch as weekly, hourly, monthly, or continuously. The ITM datapreferably includes purchase order (PO) information with regard to eachpurchase order passing through the ITM system, but may instead operatewith a subset of all of the available purchase orders. In addition tothe PO information, the ITM data preferably includes item informationfor all of the items shipped in each of the POs. The ITM data may beretrieved from a remote site such as a data center, for example. Thesystem for logistics optimization 100 may be co-located or remotelylocated from the data center.

Next, at the ITM data preparation processor 120, the item data isaggregated at the PO level. For example, individual items that werehistorically purchased during the last 60 days are combined into asingle or multiple POs representing a re-ordering of the same itemsduring the next 60 day period. Additionally, data from the PO level maybe taken and aggregated into a frequency and load size for the lane.This may be viewed as a theoretical set of POs. For example, a frequencyof four and a load size of 40,000 lbs may be viewed as four POs in thenext time period at 40,000 lbs each.

In addition to historical information, other data may be employed. Forexample, forecast information may be employed such as for highlyseasonal products, for example. Additionally, third party informationsuch as lanes that are currently serviced by a third party, eitherinbound or backhaul, may be used. Additionally, instead of PO history,distribution center use or sale of items, or other inventory informationmay be used.

Additionally, the PO data is aggregated at the lane level. A lanepreferably includes a unique combination of 5 elements, partneridentification, vendor number, original location, destination location,and temperature protection (TP). Partner identification is anidentification of the company receiving the goods. Vendor number is anidentification of the vendor selling the goods. Original location is anindication of where the goods are first picked-up or ship from.Destination location is an indication of where the goods are eventuallyintended to arrive. Temperature protection is an indication of whetherthe goods much remain refrigerated, frozen, or if no temperatureprotection is needed. Although in the above example 5 elements are usedwhen setting a lane, a greater or lesser number of elements may beemployed.

As mentioned above, the PO data is aggregated at the lane level. Thatis, for each lane, all POs associated with a specific lane during theprevious 60 days are electronically associated an identification of thespecific lane. Additionally, the data can be for lanes that the user'scompany is not currently moving, so that the tool can be used tooptimize them in order to try to bring in more freight under management.

Next, the associated data is passed to the ITM data importer 130. TheITM data importer 130 filters the data and enters the data in anoptimization module as further described below. For example, whenfiltering the data, the ITM data importer 130 presents several choicesor options to a user. The user may then filter the network as desired.For example, a user may only want a subset of the distribution centersin a network, such a subset may represent a geographic area such as theeast coast, for example. Further, a user may be interested in filteringthe data in this way for any of several reasons, for example the usermay have a new customer in the east coast area and be looking todetermine the impact on logistics in the area.

Additionally, the user may filter by temperature requirement, such asrefrigerated vs. non refrigerated, or by the other options shown in thescreen shot.

Additionally, the ITM data importer summarizes the imported data bydisplaying the imported data for a user, for example, for displaying thedata by lane. Additionally, the ITM data importer performs a qualitycheck to identify missing information in the imported data. That is, forthe imported attributes, the ITM data importer identifies the lanes withmissing information and presents them to the user. For example, for theattribute of “weight”, the ITM data importer may determine the totalnumber of lanes with no weight, and the total revenue and other aspectsassociated with those lanes, and then present those lanes to the user.Similarly, the ITM data importer may give total number of lanes missingcubic feet (cube) or pallet.

For example, the ITM data importer may identify the total number oflanes with any quality issue (missing any of weight, cube, or pallet)and provide a link to the lane so that the user can review the lane andattempt to determine what is wrong or enter in the missing data.Alternatively, the user is provided with the option to exclude laneswith incomplete data from the analysis.

Finally, the ITM data importer preferably displays a graphical view ofthe data including plot vendor maps, data center map, and lane map forthe imported data.

Alternatively, filtering of data may be done in the normal manner ofdata base based on the criteria that the user enters, that is, a usermight specify lanes going to a specific distribution center, or excludelanes with a weight below a certain amount.

Alternatively, with regard to the quality check, the quality check maydo separate summaries of lanes that are missing weight info, missingcube info, missing ship from info, etc. and then permit the user to findthe individual lanes that fall into each category. For example, thequality check may report that there are 23 lanes missing weight with atotal revenue of $53,121 per month, and 45 lanes missing cube with atotal rev of $43,634 per month, etc. The 23 lanes missing weight mayalso include lanes that are in the 45 missing cube.

Next, the imported data is passed to the constraint set-up processor140. At the constraint set-up processor, global constraints areestablished for the modeling process as further described below.However, such global constraints may later be overwritten at thescenario level and/or the model level.

Next, the scenario set-up processor 150 is used to specify models thatconstitute a scenario, as further described below. The scenario set-upprocessor 150 may also specify data and set constraints used foroptimization as further described below.

Next, at the modeling processor 160, all of the models for a specificscenario are run, as further described below. The modeling processor 160includes an optimization engine that provides optimized solutions inaccordance with the specified scenario and constraints. The solutionsfrom the optimization engine are obtained. Further, an optimization logis provided so that the optimized solutions may be stored in theoptimization log.

Next, the results of the modeling processor 160 are reviewed at themodel results viewing application 170. At the model results viewingprocessor 160, several options are available. First, a specific solutionor option may be locked or excluded based on the review. The solutionsmay also be excluded from future models so that a user need notre-reject the solution. Additionally, when locked, the solution isforced to come up again during the next solution determination. Forexample, even if the modeling processor 160 determines that a certainsolution with a certain number of specific lanes is one of the desirablesolutions, a reviewer may choose to discard the solution or discardspecific lanes for reasons not related to profitability. As anotherexample, a lane or solution may be chosen that has a specific truck in aspecific city on a specific day or on weekends so that a driver mayvisit family.

Additionally, the results viewing application 170 allows the reviewer topublish a solution or mark a solution for publication. That is, aparticular chosen solution may be shared with other people in thecompany, such as purchasing employees, to make sure that the proposedsolution provides for the needs of the purchasing employees as well—forexample, with regard to inventory turns and desired stockpiles ofinventory. The results viewing application 170 also makes a profile. Theprofile may be passed between employees to obtain consensus as to thesolution.

Also, after any change to a parameter of the model, such as locking ordeleting a solution, the model may be re-run to display a new set ofsolutions which may then be further analyzed and potentially modified bythe user as desired.

Once a desired solution is determined and agreed to by all responsibleemployees, for example, both logistics and purchasing employees, theresults publisher 180 publishes the results to the ITM database 110 tocreate a solution profile in the ITM database 110. Alternatively, thesolution profile may have already been published and solution profilemay now be made active. By publishing the results to the ITM database110, the actual logistics instructions for the company are changed. Forexample, the company's goods will now be shipped to the company based onthe shipping/logistic instructions that are now published to the ITMdatabase 110 rather than previous instructions. Note however, that thepublished results sent to the ITM database 110 may but need not changeall of the previous logistics instructions.

In addition, the results publisher keeps a summary and/or copy of theresult published to allow for later analysis and potential modification.

FIG. 2 illustrates further detail of the optimization process performedby the modeling processor 160 of FIG. 1. In FIG. 2, several individualmodels 210-208 are used to form a stack of lane solutions 210 which arethen passed to a Mixed Integer Program (MIP) Optimizer 220 to determinethe optimal solution set. Then, a final consistent optimal solution set230 is output.

More specifically, the individual models 201-208 include a one way TruckLoad (TL) model 201, a one way Less Than Truckload (LTL) model 202, aone way Inter Modal (IM) model 203, a combo model 204, a buy and fillmoel 205, a loop/continuous move model 206, a cross dock model 207, anda backhaul model 208 each of which is further detailed below.

First, the one way model Truck Load (TL) model 201 creates individualsolutions for each lane in a list and order frequency is flexed/alteredto maximize revenue per truck. In a preferred embodiment, the followingformula is employed to find the optimum frequency:

OptFreq=max(min(total monthly wt/max wt per truck, total monthlycube/max cube per truck, total monthly pallets/max cube per pallet),MinFreq)

MinFreq=min(historical freq, 1/max time between orders)

max time between orders=preset value based on inventory requirements

Where monthly weight is the total weight on the lane over all items. Maxweight is based on type of truck. Monthly cube is total cubic feet involume required for the month over all items. Monthly pallets is thetotal pallets required over all items for a month. Trucks are generallymeasured in three dimensions—weight, cube, and pallet—each with its ownmax capacity. A truck may run out of capacity due to any one of thesedepending on what is being hauled.

Alternatively, the model may be controlled to ignore one or two ofweight, cube, or pallets, but preferably all three are used whereavailable in order to better confirm a lane solution.

The max time between orders is a constraint set by the user, forexample, to make sure that minimum inventory targets are maintained.MinFreq is the minimum frequency of orders and is the lesser of thehistorical frequency or orders or the inverse of the max time betweenorders as set by the user.

The next model is the one way LTL model 202. In the one way LTL model202, individual solutions are created for each lane and placed in alist. The individual solutions use a less-than-truckload mode oftransportation. The order frequency is preferably not flexed or alteredfrom the current information. Additionally, weight, cube, and palletsper load are based on the recent historical purchase orders.Additionally, lanes are restricted based on LTL operational constraintsset by the user. Some constraints for LTL include: max weight, max cube,and max pallets. If the weight, cube or pallets are larger than the max,then the load is TL (truckload) and typically cannot be moved LTLbecause LTL carrier would typically refuse to take it.

The next model is the one way intermodal model 203. In the one wayintermodal model, 203, individual solutions are created for each lanethat will use an inter-modal method of transportation, such as bothtrucking and rail, for example. Other modes include shipping, parcel andbackhaul (BH).

With regard to backhaul, a company that owns its own fleet may havetrucks that are normally routed to a remote location to make a drop offor delivery, but are then forced to return empty to the distributioncenter from the drop off point. However, these trucks may be used tomake an inbound pick-up and delivery at very low cost since they had totravel in proximity anyway to return to the DC.

That is, instead of the truck returning to the distribution center ormanufacturer empty, the truck may be used as a carrier back. Theinclusion of possible backhaul lanes in the model may be accomplished byits own mode, called backhaul mode, or it may be implemented in othertools. For example, the combo model may combine lanes and used backhaullanes as an option. In one or more embodiments of the description ofmodels above, the trucks/equipment are owned by the distributor so as totake advantage of the backhaul opportunities.

Additionally, with regard to the “parcel” mode, the parcel mode takesinto account the shipping cost of moving parcel-size and weight itemsusing a common carrier such as UPS or the US or international mail.

In the one way intermodal model 203, order frequency is flexed using thesame formula as in the one way truckload model 201. Additionally, lanesare restricted based on intermodal operational constraints set by theuser, for example, weight, cube, and pallet, as well as also temp,length of lane, and origin and destination.

The next model is the combo model 204. In the combo model 204,individual solutions are created where each solution includes two ormore lanes. More specifically, the lanes of a solution may be identifiedbased on pick-up proximity, drop-off proximity, temperature protection,revenue generated, or restrictions based on one or more of: 1) out ofroute miles, 2) number of picks, 3) number of drops, or 4) number ofstops. All lanes in the solution are preferably set to have the sameOptFreq (that is, all lanes are picked up together each time. However,in alternatives this may be varied. The formula for determining OptFreqis preferably the same formula employed by the one way truckload model201.

The next model is the buy and fill model 205. The buy and fill model 205creates individual solutions where each solution includes 2 lanes: thebuy lane and the filler lane. More specifically, the lanes of a solutionmay be identified based on pick-up proximity, drop-off proximity,temperature protection, revenue generated, or restrictions based on oneor more of: 1) out of route miles, 2) number of picks, 3) number ofdrops, or 4) number of stops. The buy and fill model 205 is typicallyonly used for some orders and the filler lane is not always transportedwith the buy lane. Preferably the filler lane is flexed in order tosufficiently fill a truck. However, the buy lane is typically not flexedand the frequency is set to the historical frequency. For example, thefiller lane may be four purchase orders per month, each taking up 90% ofa truck while the buy lane may be one load per month taking up 10% of atruck. Once per month the two lanes may be shipped together, but 3 timesper month the filler may be shipped all alone.

The next model is the loop/continuous move model 206. In theloop/continuous move model 206, individual solutions are created whereeach solution includes two or more lanes. More specifically, the lanesof a solution may be identified based on pick-up proximity, drop-offproximity, temperature protection, revenue generated, or restrictionsbased on one or more of: 1) out of route miles, and 2) number of stops.Frequency of delivery is flexed. The loop/continuous move model 206differs from the combo model 204 in that loads are transported insequence in the loop/continuous move model 206 rather than at the sametime in the combo model 204.

The next model is the cross dock model 207. The cross dock model 207creates individual solutions where each solution includes multiple laneswith consolidation and/or deconsolidation points such as provided by across dock. In the cross dock model 207 order frequency is flexed.Additionally, many lanes can be covered in a single solution. That is,one cross dock solution may be the optimal way to move the flow forseveral lanes. This is different from one ways where one one waysolution only involves one lanes. One cross dock solution will typicallyalways involve multiple lanes.

The final model is the backhaul model 208. As discussed above withregard to the intermodal model 203, a company that owns its own fleetmay have trucks that are normally routed to a remote location to make adrop off or delivery, but are then forced to return empty to thedistribution center from the drop off point. However, these trucks maybe used to make an inbound pick-up and delivery at very low cost sincethey had to travel in proximity anyway to return to the DC.

That is, instead of the truck returning to the distribution center ormanufacturer empty, the truck may be used as a carrier back. Theinclusion of possible backhaul lanes in the model may be accomplished byits own model, called backhaul model, or it may be implemented in othertools. In one or more embodiments of the description of models above,the trucks/equipment are owned by the distributor so as to takeadvantage of the backhaul opportunities.

One aspect of the present invention is the recognition that there is aconsiderable difference between inbound logistics and outboundlogistics. For example, one or more embodiments of the present inventionprovide an achievable strategy for inbound logistics organizations toelevate freight savings by 20-30%, through a collaborative,technology-enabled approach to logistics and purchasing planning. Morethan a new set of tactics, the approach implements a paradigm shift,away from a model that tends to mimic an outbound logistics program, andtowards one that extracts full value from the advantages of inboundfreight control.

One important difference is, unlike the outbound side, Inbound FreightManagement has a revenue component, originating in the freightallowances on products provided by the shipper. If the logistics teamcan source carriers at a rate lower than the allowances, InboundLogistics can become a profit center, earning income on lanes taken overfrom shippers. Because of this, in the inbound world, load profitabilityand total landed cost (in addition to service level) are importantmetrics requiring management. Freight cost reduction, the traditionalbarometer of logistics performance, tells only half the story. Moreover,the story must be told at an item level. Logistics income is impacted bythe viability of SKU-level freight allowances in reflecting truemanufacturer freight costs, and also by the mix of items on the truck.Item-level visibility is a valuable asset while managing inbound freightand pursuing lowest total landed cost.

Another important difference is that Inbound freight programs canlargely be selective in the lanes they convert to their management.Increased profitability may be as much a question of what lanes anorganization manages—or choose to cease managing—as how well they managethem. Effective monitoring of lane profitability enables InboundLogistics departments to build the network they want, rather than managethe network they are given. In practice, organizations struggle inmatching up daily load planning to the network planning exercise thatpersuaded them to take over management of a freight lane. Propersynchronicity between these processes is important to deliverpredictability of results in Inbound Freight.

Another difference is that Inbound Freight planners work in the samecompany as the buyers placing the orders—and consequently can vary orflex the orders in terms of amount and frequency so as to maximizelogistics efficiency. This is an opportunity for collaboration betweenPurchasing and Logistics, to provide ordering guidelines that createrouting efficiencies. Equipment utilization is the largest single driverof freight cost per case, and the largest single driver of equipmentutilization is the buying pattern: how much is ordered, when it isordered, and with what frequency. Outbound shippers will attempt toinfluence purchasing behaviors through order volume price breaks, and insome instances vendor managed inventory programs. However, InboundLogistics has the far greater opportunity for true, broad-basedcollaboration with Purchasing.

The differences between inbound logistics and the typical logisticsprogram outlined above are very significant. Inbound and Outboundlogistics are, truly, entirely different business functions.Unfortunately, technology providers have largely ignored the differencesbetween them. Transportation Management System (TMS) solutions purchasedfor inbound freight management are precisely the same systems purchasedfor outbound freight, and implemented nearly identically. Little or noconsideration has been given to load profitability or per case analysis,and item-level visibility is rare.

No prior art systems address the selective nature of the freight undermanagement, the need to build synchronicity between network planning andload planning, and none expose or manage the opportunities tocollaborate with Purchasing. Put simply, in commercial transportationmanagement systems, the world is seen through the lens of a manufacturershipping outbound product. This is the arena in which the products havebeen developed and tested, and it represents the largest market segmenttheir sales forces pursue. As a result, inbound logistics personnel areforced to fit within the mold of outbound transportation managementprocesses, or struggle to change or augment those capabilities to meettheir objectives.

When it comes to collaboration between Purchasing and Logistics, withoutthe right tools, most supply chain organizations find limits to whatthey can achieve. Absent a well-defined and technology-enabled platformfor partnership, these highly inter-dependent functions remain at arm'slength, communicating without collaborating, bound to different andoften conflicting incentives.

When it comes to locating and quantifying the potential savings byintegrating inbound logistics with purchasing, one of thedifferentiating aspects of inbound freight matters above all others:control of the freight resides in the same organization as control ofthe order. The potential power of this is easy to understand, in theory.After all, if logistics personnel placed the orders, every truck wouldbe 100% utilized, every time (or better yet, running on rail). Back inthe real world of changing customer demand, short product shelf life,inventory carrying costs, and storage capacity constraints, a separatepurchasing and inventory control function is required.

However, there is a middle ground where a deeper logistics savingsconsideration can become a greater part of purchasing operations. Fewwould disagree that if purchase orders are aligned to more consistentlyfill trucks to capacity and minimize miles driven, logistics costs willimprove. However, purchasing and replenishment systems that includefreight cost consideration do so at only the most rudimentary levels, ifat all.

If a supply chain leader asks the question: “What is the absoluteminimum total landed cost that can be achieved by the combinedorder-to-delivery process, without putting customers at risk?” mostPurchasing and Logistics teams do not have the ability to answer.Instead, current systems rely on the following three assumptions:Assumption #1: Purchasing needs no further guidance. Our Buyers alreadytry to order in full truckloads wherever they can; Assumption #2:Logistics' requests for order pattern changes will generally beinfeasible, as they do not consider customer demand; and Assumption #3:Since logistics savings are based on freight consolidation, everyattempt to save in freight costs will come at the expense of inventorylevels. These assumptions come to rule the relationship betweenPurchasing and Logistics. As is often the case with deeply embeddedassumptions, they are self-fulfilling: they quash any momentum to fullycollaborate in driving savings, thereby limiting logistics to offer onlythe most rudimentary and uninformed purchasing guidelines, which onlyappear to further prove out the assumptions. The guidelines, born in themanual spreadsheet manipulations of a logistics engineer, tend only toincrease order sizes and reduce inventory turns (putting them atimmediately odds with Purchasing performance metrics), and often ask foralignment of orders in ways that will risk stocking out of a product. Inpractice, a few vendors may be found that both sides agree can beregularly scheduled to deliver simultaneously, but even these requestsfrom logistics are frequently ignored in favor of daily decision-makingon the part of the buyer. As embedded as it is, this is a cycle ofbehavior that can only be broken with a clear measure of the value ofbreaking it.

FIG. 3 illustrates how the present system for logistics optimization 100using lane order pattern flexing may provide a savings increase of20-30%. FIG. 3 includes a current delivery pattern 310, a currentshipment truck fill 320 and a current order summary 330. FIG. 3 alsoillustrates a new delivery pattern 350, a new shipment truck fill 360,and a new order summary 370, as well as a logistic results summary 380and a route map 390. Additionally, the items carried by the trucks aredifferentiated into product A and B based on their shading. Further,although only two items and a single route are shown, FIG. 3 is meant tobe a simplified example of the present system for logistics optimization100.

Turning to FIG. 3, the current delivery pattern 310 illustrates that thecompany receives in a 20-day period two deliveries of product A and fourdeliveries of product B. The frequency and days of the weeks of eachdelivery are shown. As shown, none of the six total deliveries takeplace on the same day.

Turning to the current shipment truck fill 320, is it shown that the twodeliveries of product A take place using a truck that is 90% filled,while the deliveries of product B take place using a truck that isanywhere from 45% to 75% filled. Such a situation may occur often in thereal world where product B's usage over the month or the demand forproduct B over the month is non-uniform.

The current order summary 330 reveals that the current logistics processto deliver items A and B uses 6 trucks which are on average 67% filledand 3600 total miles are driven per month. The cost for these trucks tomake the deliveries is $7700 per month in this example—although thisnumber may vary depending, for example, on route, temperatureprotection, and truck size.

In other words, the left side of FIG. 3 represents a sample currentstate: freight running on two lanes on a monthly basis, both droppingoff at the same facility. One product is ordered in near full truckloadquantities, twice a month. The other is ordered in smaller quantities,required at least four times a month. Assuming that no other shipmentsexist that could fill out the trucks on the second lane, both Purchasingand Logistics would typically claim comfort with the current state. Thebuyer is filling equipment where they can, and only ordering smallerquantities where they must.

On the Logistics side, the prior art TMS route optimization softwareleaves the full truckloads alone (no TMS system on the market ever seeksto break a truckload shipment), and sees no way to improve upon thesecond lane. Logistics engineers may ask Purchasing to place largerorders on Lane 2 for product B, only to be told that inventory turnscannot be increased without risk of stocking out.

Turning to the new delivery pattern 350, it shows a new delivery patternin which there are only four deliveries during a 20 day period and eachdelivery includes a delivery of both Product A and Product B. Turning tothe new shipment truck fill 360, it is seen that each of the newshipments is composed of about 40% of Product A and about 60% of ProductB.

Thus, the two approximately full truckload shipments of Product A havebeen broken into four shipments of partial truckload and the remainderof each shipment is filled with Product B. As shown in the new ordersummary 370, the new plan only involves four trucks rather than 6, andeach of the trucks is about 99% filled. Further, the monthly cost isabout $6150 and the miles traveled is about 2900.

The improvement of the new delivery pattern over the old deliverypattern is summarized in the logistics results summary 380. Morespecifically, two fewer trucks are used, the trailers of the trucks thatare used are much more fully utilized—up to about 99% from 67%, there isa $1500 per month savings (20.1%) and 700 fewer miles are traveled inall.

FIG. 4 illustrates the example of FIG. 3 at an inventory rather than alane level. FIG. 4 shows the current replenishment pattern 410, currentorder 420, and current order summary 430, as well as a new replenishmentpattern 450, new order 460, new order summary 470, purchasing results480 and route map 490.

As shown in FIG. 4, the current order replenishment plan 410 shows thecurrent order 420 is delivered on six different days and that thedelivery amount of Product B varies. As shown in the current ordersummary 430, the current order provides six total inventory turns andprovides an average of 21 days on hand of Product A.

Turning now to the new order, as shown in the new replenishment plan450, deliveries are down to four days and both Product and Product B aredelivered together. Further, a smaller amount of Product A is deliveredin each of the four shipments and the deliveries of Product B are set toan average number, as shown in the new order 460. As shown in the neworder summary, the new order 460 represents eight total inventory turnsand reduces Product A to 17.25 days on hand. Finally, as shown in thepurchasing results, the new order 460 has increased the overall turns by33%, reduced the inventory of Product A by 18, and made the orderpattern of Product B more predictable.

In other words, by scaling down the truckload orders to free up enoughspace to absorb the shipments on the second lane, a new picture emerges:four full multi-stop truckloads a month. This concept goes against priorart TMS systems which would not break up the shipments of Item A becausethey are approximately a full truckload. The results of the example ofFIG. 3 include: 20% reduction in freight cost, 60% increase in overallinventory turns, 33% reduction in deliveries hitting the dock, and 19%reduction in miles driven.

These results are very beneficial, and not just in the savings theydeliver. Importantly, they protect and even improve upon key purchasingmetrics as well. Add to this the operations benefit of reduced dockcongestion, and a significant carbon footprint improvement, and thisexample begins to speak loudly for a new way of thinking about logisticsability to impact supply chain objectives. The example shatters theassumption that logistics savings only comes at the expense of inventoryrisk. In fact, all three assumptions in the prior section are challengedin this one example, for one very counterintuitive reason: scaling downorders can improve logistics efficiency.

The present logistics optimization system considers the full range ofpossible adjustments to order size, frequency, and timing toexponentially increase the possibilities to mine for freightconsolidation. Unlike the old method of route optimization alone thatwaits for matching shipments, combined optimization of ordering androuting essentially lets the user match shipments as desired.

The present logistics optimization system may expose and assess theuniverse of permutations of ordering and routing. When the presentoptimization algorithms are employed to uncover these “win-win”scenarios, the results can be surprising in scale. Assessments ofinbound freight networks large and small have shown that solutions suchas the example above are so prevalent in a network that the network-widesavings of 20-30% is accompanied by an average total inventory reductionof 1.5%. This inventory reduction is a net number, inclusive ofsolutions that scale orders up (within reasonable constraints, such asmaximums of 3-4 weeks inventory) or scale orders down. This means thatthe impact of scaled down orders is outstripping the impact of scalingthem up. While these results can vary from one inbound network to thenext, most organizations can minimally expect to keep inventory levelsflat while still achieving significant savings.

The logistics changes found by the present logistics system may beimplemented without significant process or systems upheaval. In apreferred system, buyers still place the orders, using existing systemsand logistics planners still plan the routes, using existing TMScapabilities. Collaboration preferably does not require any change tothe fundamental responsibilities or personnel makeup of these teams. Italso does not require a disruption in the flow of orders from purchasingto transportation systems. Instead, implementation is building newconnective tissue between purchasing and logistics processes, based onup-front planning and a closed feedback loop for compliance monitoringand corrective action. The connective tissue is found in specific newactivities and technologies at three junctures in the order-to deliverysequence: prior to order, prior to tender, and post-delivery.

FIG. 5 illustrates the addition of the present system for logisticsoptimization 100 into the logistics process 500. As shown in FIG. 5, thelogistics process 500 includes sales 510, purchasing 520, and logistics530. Sales 510 includes the function of creating a forecast 515 ofinventory or products needed. Purchasing 520 includes the function ofcreating an order 525 to obtain the desired inventory or products.Logistics 530 includes the functions of tendering the load 532 anddelivering the load 535. Additionally, one or more aspects of thepresent invention may interact with the logistics process 500 at one ormore of prior to order 550, prior to tender 560, and post-delivery 570.

With regard to the interaction of the present logistics system 100 withthe logistics process prior to order 550, the most expedient way toadjust order patterns and set routing guidelines is to do so with aplanning-based approach, pro-actively, before the orders are placed.This periodic planning process is performed on the side of the existingbuying and freight execution sequence. It is certainly possible toimplement a more invasive and exacting process, generating replenishmentorders systematically that consider forecast, inventory, and logisticsimpact. However, if the intention is to capture the bulk of thesesavings with the minimum of systems and process turnover (as is likely),a planning-based approach is advisable.

In a planning-based approach, a technology solution is leveraged, likelyby a Logistics Engineering person or team, to periodically examinedemand requirements, based on recent order history, updated with anyseasonal or other demand forecasting information. This process might berun once a week, once a month, once a quarter—the frequency depends onnetwork volatility, and how tightly the organization wants to manage theordering guidelines to support the highest profitability. The logisticsoptimization system accepts order history, forecast information, andcarrier rate information, and uses optimization technology as describedabove to identify the most profitable ordering and routing scenariosavailable for each freight lane.

Constraints may be applied at a global, supplier, and item level to markthe boundaries of feasibility. Some constraints likely to be requiredinclude (but are not limited to) equipment type, limitations on productsthat cannot be consolidated, pallet space, and on the order patternside, shelf-life restrictions, and the degree to which order frequencycan be adjusted.

The output of this process is not orders or loads. It is a set ofguidelines on how to purchase and route product: recommendations onorder size, frequency, and timing, to set up ideal consolidationsolutions. The optimization technology accounts for the opportunitiesavailable to your network, by leveraging multiple models as describedabove. This may further include backhaul opportunities and fleetutilization, continuous moves, and cross-dock or pooling scenarios.

A process is then implemented to review, approve, and “publish” theseguidelines. This involves software-supported workflow to track agreementfrom both Logistics and Purchasing, and signoff on the savings andinventory impact for each solution. Once published, the guidelines arefed to purchasing, for adoption during the replenishment process. Mostrobust purchasing systems may accept the types of parameters required,but some buying organizations may be more comfortable using them in amore manual fashion. In addition, the profitability expectations of eachsolution are stored, as targets to be measured against later in theprocess.

The present proactive planning process is typically notresource-intensive for each implementation. The first time it is run,the entire network is under review, and the list of solutions to assessquite long. From that point forward, the full network is preferablyincluded in the optimization process, but only the resulting solutionsthat are new or changed need enter into the review and approval process.This is typically a manageable list, on the order of 3 to 5% of totalfreight lanes on a monthly basis, even in large-scale networks.

In fact, the overall resource impact of this approach can be veryfavorable. Today, many organizations leverage optimization technologywithin their selected TMS solution to select routing for freight justprior to load tender. The simpler solutions that emerge from thisprocess can largely be tendered with little oversight. However, freightplanners often find that they need to review all suggestedconsolidations that emerge from these tools, to ensure feasibility.Despite the promise of automation, too many business exceptions exist topermit this sort of hands-off freight routing. In contrast, an up-frontplanning approach seeks to smooth out and standardize purchase orders,such that route determination more often follows a plan that has alreadybeen vetted. In an environment of collaboration between Purchasing andLogistics, daily exception management at the point of freight executionis significantly reduced, in favor of a more efficient, proactiveplanning regimen. Before moving on, it should be mentioned that theplanning function can and should be leveraged to examine freight that isnot yet under management, where a freight allowance is known (or a truefreight cost has been broken out). Completely separate from the 20-30%savings improvement stated earlier is the added revenue achievable byfinding new lanes that fit with the buyer's network. In many instancesthese are lanes previously ignored as unprofitable, when order patternchanges were not considered.

With regard to the interaction of the present logistics system 100 withthe logistics process prior to tender 560, it is recognized that lastingsuccess in any collaboration activity requires more than just a jointplanning function. A closed feedback loop is desirable to monitorcompliance to plan, and support timely corrective action between bothteams. Since this solution involves building better order patterns upfront, it is possible within this model to recapture load profitabilitybefore it is even lost (i.e. shipped).

This may be done by leveraging exception management technology tohighlight non-compliant purchase orders as soon as they are created, andfacilitate communication between load planners and buyers to revise theorder before it is built into a shipment and tendered. There is no needfor this process to interrupt the automated flow of orders to a TMSsystem, as long as the compliance alerts are acted upon before thetender occurs. This may often be accomplished through simple processtiming (checking compliance alerts prior to running the load creationprocess in the TMS).

Not all instances of non-compliance may require action. Some may arisefrom unanticipated inventory needs. Some may be close enough to targetthresholds that a decision can be made to allow the order through. Somemay simply highlight that a plan needs to be changed for future ordersto reflect new realities. To facilitate this decision process, it may beimportant that the exact reason for non-compliance and the profitabilityimpact (dollar variance from target) is available with the complianceexception alert. It is also desirable to log reason codes whenever anon-compliant order is allowed through, to facilitate summary reportingof process effectiveness.

This “soft checkpoint” (soft, meaning that orders are not automaticallyadjusted to be compliant), along with the periodic re-assessment ofplans discussed earlier, enables order patterns to be changed in a waythat is still responsive to a dynamic supply network. As validexceptions occur, they are allowed through, but measured, and ifrepresentative of the new operating rules, used to trigger updates tothe plans.

With regard to the interaction of the present logistics system 100 withthe logistics process post delivery 570, the final step in theclosed-loop process is trend reporting at a lane level, and root causeanalysis on the margin of delivered loads. A host of factors may reduceload profitability from the targets set during planning, includingfreight allowance changes, order size fluctuations, and product mix onthe revenue side, and secondary carrier usage, fuel rate changes, andone of a host of possible unplanned accessorial charges in the loadcost.

In depth visibility and drilldown root-cause analysis into these driversis desirable for any inbound freight management team (even those nottaking this approach in full), as well as a tracked workflow process toensure that steps are taken to prevent or offset margin decay over thelife of a freight lane. It is noted that commercial TMS solutionslargely neglect freight margin analysis. A few may carry PO-levelrevenue through, but cannot measure the impact of item mix and lack theability to drive to SKU-level analysis. Without the capability toperform detailed root cause analysis into both revenue and costmovement, inbound freight management teams may struggle to maintain arigorous focus on sustaining savings.

FIG. 6 illustrates a screen shot 600 of an Inbound Transport Management(ITM) system according to an embodiment of the present invention. Asshown in FIG. 6, the screenshot 600 includes project information 610,such as a name and description, the dates created, modified, andpublished, and any status.

The screenshot 600 also shows a set up section 620 including data forseveral lanes. Each lane preferably includes information about the typeof data, the data set name, the date it was loaded, the data it may havebeen modified, and the number of records. A list of at least some of theproject constraints is also shown at 622.

The screenshot 600 also shows an optimize section 630 including severalscenarios for consideration for implementation. Each scenario ispreferably associated with an ID, a name, a run history, the number ofsolutions, the monthly savings, and the schedule. Additionally, asummary of scenario results is shown at 632.

The screenshot 600 also shows a publish section 640 including a listingof scenarios that have been published. Each published scenario ispreferably associated with a date, name, person publishing, savings, andsolutions.

FIG. 7 illustrates a screenshot 700 of the ITM lane import criteriascreen. As mentioned above with regard to FIG. 1, recent historical lanedata is imported into the ITM system. As shown in FIG. 7, the recenthistorical data includes lane and vendor numbers, vendor name, ship-fromcity, freight allowance, weight, and cube, monthly frequency, andseveral other factors.

Additionally, FIG. 7 illustrates the “grade” column. The grade columnrepresents a grade that is manually by a reviewer to indicate lanes thatare more profitable than another, for example, for review and discussionof taking over such lanes. Alternatively, the grade may be assignedbased on profitability and risk of execution of the lane or solution.

FIG. 8 illustrates a screenshot 800 of the constraints preferablyentered for the implementation of the combo model 204 of FIG. 2. Asshown in the screen shot 800, the constraints preferably include loadsize constraints, lane constraints, financial constraints, costsettings, flexing constraints, and solution constraints. Similarconstraints may be entered for each of the models 201-207 of FIG. 2.

FIG. 9 illustrates a screenshot 900 of an ITM scenario analysis screenproviding a view of optimization solutions with the ability to lock,exclude, and mark solutions for publication. The screenshot 900 includesset up information 910 including lane data and scenario constraints andoverrides. The screenshot 900 also includes optimize information 920identifying each model employed, a description of the model, anyconstraints employed, the cost savings, and the model results. Thescreen shot 900 also includes scenario solution detail information 930including status and information about the solutions.

FIG. 10 illustrates a screenshot 1000 of a lane profiles tool forvisualization and what-if analysis of lane optimization and orderflexing results. From the screenshot of FIG. 10, the user may view andmodify drivers financial, operations, and inventory impact of one-way,consolidation (combo), cross-dock, backhaul solutions. The user may alsoview related solutions and access lane and item information on recentsales, purchase, and inventory. The user may also manage workflow inactivating solution.

Further, as shown in the screenshot 1000 of FIG. 10, it includes a laneidentification 1005 and a financial and operations summary 1010 thatincludes new order frequency, size, and estimated revenue, cost, margin,and inventory impact. The screenshot 1000 also shows a list of lanesincluded in the solution profile 1020 with summary statistics related topurchase patterns and freight financials. The screenshot 1000 also showsa list 1030 of other profiled that include lanes in this profile.

FIG. 11 illustrates a screenshot 1100 of a lane analysis tool used toexamine shipment, purchase order, inventory, and sales informationsummarized to a lane level to support order and route patterndetermination. Additionally, the screenshot 1100 shows a lane-levelsummarization 1110 of order/shipment information and product sales, bymin, max, average, etc. across recent history. Further, the screenshot1100 shows recent purchase order activity 1120.

FIG. 12 illustrates a screenshot of lane order profiles which includepurchasing guidelines for communication to purchasing systems orprocesses. The purchasing guidelines may include order size, timing, andfrequency rules for one or more lanes in a routing solution. Thescreenshot includes solution (load) level rules 1210, and lane (PO)level rules for Pickup #1 1220 and Pickup #2 1230.

FIG. 13 illustrates a screenshot 1300 of the compliance detail. Thecompliance detail displays expected and actual purchase order size,timing, frequency, revenue, cost, and highlights elements ofnon-compliance with order rules, to enable logistics or purchasingpersonnel to assess the financial impact and consider correcting ordersprior to load tender.

FIG. 14 illustrates a screenshot 1400 of the gross margin dashboard. Thegross margin dashboard enables root cause analysis of margin shortfallsagainst target, showing performance against target, over time, byrevenue and cost component/driver. It also enables view of only negativeimpact components, to identify improvement opportunities whether or notthe load met margin targets at an overall level. Further, the grossmargin dashboard allows drill down into lane and shipment level toexamine root cause and allows the user to filter by “lane issue”, aworkflow mechanism for tracking resolution of issues found.

As shown in FIG. 14, the gross margin dashboard 1400 allows a user toclick on an entry such as the Jul 11 “Lower Load Qty” entry. A summaryscreen 1410 detailing those loads having lower load quantity is thendisplayed. Additionally, the summary screen 1410 allows a user to clickon a specific load to display a detail screen 1430 displaying detailsfor that particular load.

FIG. 15 illustrates a business information flow 1500 according to thepresent Inbound Transportation Management (ITM) system. The businessflow 1500 includes an Information Management System (IMS) Platform 1510including an ITM Profitability Optimizer 1512 and an ITM Profitabilitymanager 1514. The business flow 1500 also includes a TransportationManagement System (TMS) 1520 and the Purchasing department 1530.

As described above, order history 1550 is passed to the ITMProfitability Optimizer 1512 which generates a solution of optimizedlogistics representing a set of lane profiles 1552 and passes the laneprofiles to the ITM Profitability Manager 1514. Additionally, the ITMProfitability Optimizer 1512 passes order rules 1554 back to thePurchasing department.

The purchasing department 1530 then places orders 1560 with the ITMProfitability Manager 1514. The ITM Profitability Manager 1514identifies exceptions to the lane profile in the purchase orders 1560and passes an identification of exceptions 1562 back to the purchasingdepartment for review and potential modification to conform to theoptimized lane profile.

Once the compliance exceptions have been resolved, the ITM ProfitabilityManager 1514 relays purchase orders and routing instructions 1570 to theTMS 1520. The ITM Profitability Manager 1514 also receives data withregard to the actual shipments 1572 back from the TMS 1520. The receiveddata may be used to recalculate a new optimal lane pattern or to performa root cause analysis on variance against profitability targets andinitiate corrective action.

FIG. 16 illustrates the combo model stack generation process accordingto an embodiment of the present invention. The combo model 204 wasreferenced in FIG. 2. In general, as further described below, the combomodel stack generation process first filters the lanes, then creates aproximity list of pairs of lanes, then creates a list of base 2 lanecombos, then filters to solution the list for 2 way combos, then createsa list of base 3 lane combos, then filters to solution the list for 3way combos, and repeats the process up until the N-way combos where N ispre-selected by a user.

Turning to FIG. 16, first, at step 1605 the list of lanes with lane datais compiled. Next at step 1610, the total list of lanes is filtered toselects a list of usable lanes. Lanes may be unusable for severalreasons, such as a shipment date outside the current stack date.

Next, at step 1615, a pairwise proximity list is formed. Proximity ispreferably defined by the distance between picks and drops. The distanceis preferably calculated by using the havesine formula:

a=sin²(Δlat/2)+cos(lat ₁)*cos(lat ₂)*sin²(Δlong/2)

c=2*a tan 2(√a,√(1−a))

d=R*c where R is earth's radius

The pairwise proximity list is then used to form a base list of 2 waysolutions at step 1620. Solutions may be filtered out at step 1625 ifthe proximity exceeds a value set by the user or for other reasonsdescribed below. The remaining solutions are added to the stack 1630.

Additionally, the model may filter out a solution based on one or moreof the following: out of route miles, value of revenue on lane, value onlane vs cost on lane, quantity on trucks, margin, or other criteria. Themodel may also provide exact filtering based on whether lanes arereviewed in order by length, by revenue, or by quantity on truck.

Alternatively, solutions may be filtered out based on zip code regions.For example, the distance between a zip code in CA and a zip code fromNJ is available from a lookup table or other database, and may bedirectly compared to the desired proximity.

With regard to the three-way base list, the three way base list iscreated by adding lanes to the two way list a step 1635 and thenfiltering the lanes for the desired solutions at step 1640. These lanesmay be selected based on the proximity list and is preferably inproximity for both current lanes in the list. Similarly, a four way baselist may be constructed by adding an additional lane to the three waybase list using the proximity list at step 1645 and proximity for theprevious lanes on the list and then filtering the list at step 1650.This process may be repeated up to an N-way base list where N may be setby the user. The stack 1630 of all available solutions may then bereviewed and ordered by a criteria such as margin to obtain the desiredsolution.

With regard to calculating margin, the solutions described abovepreferably include a calculation of margin as well. Margin is viewed asrevenue minus cost. In this case, the cost estimate is based on dollarper mile cost for traveling from the pick up to the drop off pointsupplied in original data times the total miles for the shortest route.

Further, when determining the combo, the transported products aretreated as first in last out so that the last product loaded on thetruck is the first product out. However, in an alternative embodiment,it is allowed to remove this constraint. Additionally, in the combomodel, there is preferably always a lane in the combo where the pick upfor the lane is the first pick up for the route and the drop off for thelane is the last drop off for the route.

Additionally, in the model, the route is determined optimally byexamining combinations with reductions due to the final lane in routebeing constrained to be shorter than initial lane in route. If not, thenone can simply reverse the entire pick sequence and get a shorter totalroute, which implies that the route was not optimal. This cuts thenumber of potential combinations in half. For example if lane A>laneB>lane C in length then only need to check ABC, ACB, and BAC.

Additionally, a combo may be included in the list of bases but not inthe list of solutions because the margin is too low for the solution.Additionally, a combo might be excluded from the list of bases becauseit has such a low margin that no possible future lanes will bring thevalue up to the required threshold margin.

With regard to calculating frequency, the following formulas may beemployed:

OptFreq=max(min(total monthly weight/max weight per truck, total monthlycube/max cube per truck, total monthly pallets/max cube per pallet),MinFreq)

Total monthly weight=sum of weight over all items over all lanes

Total monthly cube=sum of cube over all items over all lanes

Total monthly pallet=sum of pallets over all items over all lanes

MinFreq=min(max(historical freq by lane), 1/max time between orders)

max time between orders=preset value based on inventory requirements,can be a function of temp

max(historical freq by lane)=largest freq on any individual lane

Additionally, frequency may be restricted if a lane is marked as “do notflex”. In such a situation, the frequency of the solution is not allowedto drop below the current frequency of the lane. This impliesMinFreq=max(min(max(historical freq by lane for lanes that can beflexed), 1/max time between orders), max(historical freq by lane forlanes that cannot be flexed)).

For example, if a combo is composed of Lane A and Lane B and Lane A ismarked as “do not flex”, and the frequency for lane A=freqA and thefrequency for lane B=freqB then MinFreq=max(min(freqB, 1/max timebetween orders),freqA).

FIG. 17 illustrates a closed loop purchase order compliance system 1700according to an embodiment of the present invention. The compliancesystem 1700 includes a compliance monitor 1710, a database of masterdata 1720, a database of subroutes 1730, a purchasing system 1740, and atransportation management system 1750.

As described above, the compliance system 1700 may be incorporated inthe ITM Profitability manager 1514 as shown in FIG. 15. Alternatively,the compliance system may be implemented on its own.

In operation, the compliance monitor 1710 receives information from thedatabase of master data including: an item master, a vendor master,ship-from information, ship-to information, and carrier rates. Thecompliance monitor 1710 also received information from the database ofmaster data 1720, which includes predetermined guideline/targets for howto order and route products. For example, the compliance monitor 1710may receive lane order rules, lane revenue and cost component dollartargets.

The compliance monitor 1710 then received a purchase order or a purchaseorder line item from the purchasing system 1740. The compliance monitor1710 then matches the purchase order to the most profitable “intended”subroute as received from the database of subroutes. The compliancemonitor also locates purchase orders or any other lanes included in thesolution, for example to complete an intended consolidation, acontinuous move, or some other aspect.

The compliance monitor 1710 then compared the purchase orders to laneorder rules for the subroute. These lane order rules may include: 1)order size including min/max weight, cube, and pallets; 2) combinedorder size including min/max weight, cube, pallets, and revenue; 3)order timing including transit time, pickup/delivery day & time windows;and 4) order frequency including availability of other purchase ordersinvolved in the solution, for example when there is a consolidation orloop in the solution.

The compliance monitor 1710 the initiates an exception alert forpurchase orders out of compliance with subroute lane order rules. Thepurchase order non-compliance alert is sent to the purchasing system1740. At the purchasing system, purchasing personnel may update andresubmit the order back through the compliance monitor. Whentransmitting the alert to the purchasing system 1740, the compliancemonitor 1710 preferably links the alert with an interface describing thenon-compliance and showing targeted and estimated freight margin inorder to demonstrate the impact of the non-compliant decision.

Finally, the compliance monitor 1710 allows compliance purchase ordersthrough to the freight execution process or transportation managementsystem. For non-compliant purchase orders, the compliance monitor 1710also allows them through if a reason code is supplied for reportingpurposes. The compliance monitor also includes routing instructions andrevenue/cost targets.

FIG. 18 illustrates a compliance reporting system 1800 according to apreferred embodiment of the present invention. The compliance reportingsystem 1800 includes the compliance monitor 1710, database of masterdata 1720, database of subroutes 1730, purchasing system 1740, andtransportation management system 1750 of FIG. 18 and adds the compliancereporting processor 1810, purchasing management process 1820 andlogistics management process 1830.

The compliance reporting system 1800 of FIG. 18 picks up where thecompliance monitor 1710 of FIG. 17 left off. That is, after thecompliance monitor 1710 passes purchase orders, routing instructions,and revenue and cost targets to the transportation management system1750, the transportation management system 1750 initiates the desiredtransport and monitors the real-world result. That is, thetransportation management system 1750 records the actual shipmentrouting and the actual shipment costs by cost component.

The actual shipment routing and the actual shipment costs by costcomponent are then passed to the compliance reporting processor 1810.The compliance reporting processor also retrieves from the database ofmaster data 1720 the item master, the vendor master, the ship-frominformation, the ship-to information, and the carrier rates.Additionally, the compliance reporting processor 1810 retrieves from thedatabase of subroutes 1730 the lane order rules and lane revenue andcost component dollar targets.

The compliance reporting processor 1810 then compares the actualshipment routing and actual shipment costs by cost component with theoptimized information from the databases 1720, 1730 and generates asummary/trend report of the purchase order/route compliance and freightmargin impact. The report is then preferably passed to the purchasingmanagement 1820 and the logistic management process 1830 for use infuture purchasing and logistics activities.

Further, in one or more embodiments, the compliance system describedabove creates a user interface that allows the user to search, sort, andmanage POs/Loads against established ordering and routing rules.Additionally, the user interface provides a list of POs/Loads that matcha lane order profile on an active subroute, a list of POs/Loads that donot match a lane order profile on an active subroute, and alerts userswhere orders are off from planned targets, and allow user to setnon-compliance and ignore reason codes. The user interface alsopreferably has a home screen Widget that summaries, by Partner, thenumber of the POs/Loads with a non-compliance status, that is pendingaction by a user.

In one embodiment of the ITM above, the ITM may only requires that thereis a single intended subroute per route. When active subroutes arechange from any status to intended, there is also a prompt screen toswitch the subroutes. In an alternative embodiment, the requirement isremoved and a route is allowed to have multiple subroutes types. Foralternative and unintended subroutes, the alternative embodimentpreferably still requires the user to link to an intended subroute underthe route.

FIG. 19 illustrates a chart of the order compliance alerts. In analternative embodiment, an order compliance alert group is set up thatincludes tolerance setup capabilities to measure POs/Loads against laneorder profiles. These options will define the parameters for creatingorder compliance alerts: 1) On or off options including GlobalActivation, Alert Buyer?, Alert Planner?, Alert Ops Support?, and AlertAccount Manager?; 2) Tolerance—weight or percentage below and/or abovethe min/max from the Land Order Profile including: Order Rules: Weight,Order Rules: Cube, and Order Rules: Pallets; 3) Tolerance—dollars orpercentage below the min from the Lane Order Profile including: OrderRules: Revenue; 4) On or Off selection including Order Rules: TransitDays, Order Rules: Transit Time, Order Rules: Lead Time, and Revenue vs.Target; 5) Tolerance—dollar or percentage above the line haul cost fromthe Planning Summary from the Subroute including Line Haul vs Target; 6)Tolerance—dollar or percentage below the load margin from the PlanningSummary from the Subroute including Margin vs. Target.

When customer purchase orders are first received through the data feed,as well as changed over time, each order goes through a validationprocess to measure ordering pattern against available lane orderprofiles. After each validation check, the IMS stores generationalinformation to track historical details including: PO and Load summarydetails—(total cube, wgt, cases, revenue, exp, marg, etc);Non-compliance and/or ignore PO reason codes; User and timestamp;Update—creation or changes to the order/load; Code—non-compliance orignore code set by a user; Compliant or non compliant status; Active orclosed status.

FIG. 20 illustrates the order compliance screen. As shown in FIG. 20,the order compliance screen includes the following search criteriaoptions: Partner Master, Partner (multi-select), Vendor Name, Vendor(multi-select), Route (multi-select), Subroute, Ship via, Planner,Buyer, Ops Support, Account Manager search, PU/Deliv/Create search,Compliance Status—based on current status (Compliant, Non-Compliant),Alert Status—based on current status (Active, Closed), “ShowGenerations” checkbox—to show historical generational statuses,Non-compliance reason code (multi-select), Ignore PO reason code(multi-select).

As new customer purchase orders are created or changed by an update fromthe daily file feeds, then the first check is to find an availablesubroute that has a matching lane order profile to the available pos.The subroute(s) available should be in active status. The subroute(s)must have a usage of Intended or Alternative. The subroute(s) must havean associated Lane Order Profile (LOP). The associated Dist/Vend andpurchase order must be flagged as “Managed”.

If the PO has found a matching subroute/lane order profile (LOP), thepurchase order(s) are matching the LOP, and the load totals are matchingthe LOP load minimum amount, then it is set to the statuses of“Compliant” and “Active”. This display shows groupings of opencustomer-pos that should be built into loads.

For the POs that did not find a matching subroute/LOP, the purchaseorder(s) are not matching the LOP, and/or the load totals are matchingthe LOP load minimum amount, then the statuses becomes “Non-Compliant”and “Active”. On the order compliance return screen, then the alertcolumn displays an indicator (spy glass or other icon) that the userwill need to process the order. When the user selects the specific orderor multiple orders, then a detail screen opens to allow the user to seethe details. For the order or selected multiple orders, then the detailscreen pulls together all of the available routes and subroutes, theseorders could fall under.

The detail screen also links to certain system records including: PONumber: link to the cust po; Load number: This is only applicable whenthe load has been create; Subroute: link the subroute screen; Vendor:Links to the Dist/Vend; Display the PO and Load summary details (weight,cube, cases, etc); Display the ship via; “PO Import” link to the poimport screen.

FIG. 21 illustrates the PO import screen. When the user selects the POimport screen, the following fields are pre-populated to direct the userto the appropriate purchase order—PO number to the identified alerts—DVDriver—Cust & AS Pos. Additionally, the purchase order is auto-searchedas to display the available subroutes. and the lane is selected in theupper panel which will display the subroutes in the lower panel.Additionally, the subroute lower panel is set to an expanded view.

FIGS. 22 and 23 illustrate two examples of the order compliance alertdetail screen. As shown in the figures, the reason for non-compliance isdisplayed by subroute. Reasons for non-compliance include: 1) Weight:Thenumber of pounds over or under tolerance; 2) Cube:The cube over or undertolerance; 3) Pallets: The number of pallets over or under tolerance; 4)PO Revenue: The $ value under PO revenue tolerance; 5) Transit:Indicator if the PO is not meeting either the transit days, transitdates, and/or lead time; 6) Missing PO: Indicator that there is at leastone more order that is not aligned with the ordering rules (transit daysand time are not matching); 7) Line Haul: The $ value over Load linehaul tolerance; 8) Margin: The $ value under margin tolerance; 9) If nocarrier rate is assigned to a subroute or is not assigned to a createdload, then the “line haul” and “margin” measurements have a an indicatorthat information is missing; 10) Missing LOP: Indicator that thesubroute has been matched from Section 2, but does not have a lane orderprofile to check ordering rules.

There are two additional special indicators that are highlighted if theyoccur: 1) Ship Via Mismatch: Indicator that the PO and Dist/Vend havemismatching managed flags. Still display the options of Route/SRsdefined above; 2) Missing SR: Indicator that there is no availablesubroute. This occurs only if the Dist/Vend is marked as available. Rule1 still applies.

For each purchase order the user is able to assign a non-compliance orignore PO reason code. For example-Non-compliance dropdown: User canselect a non-compliance code that is normally set during the importprocess. This is then stored on the AS Po for reporting purposes.Alternatively, Ignore dropdown: User can select an ignore code that isnormally set from the PO import screen and stored on the customer PO forreporting purposes. Additionally, the user can group the previouslyselected orders to combine in the theoretical load that is just aholding measure for knowing to realert the user post load build.

Additionally, when the load is created, in one embodiment a secondvalidation process is run to make sure that the appropriate loads havebeen created. In this case, if the load has been created to a validsubroute, with the correct matching details to the lane order profile,then the status is changed to “compliant” and “closed”. However, if theload has been created to a valid subroute, but with the incorrectmatching details to the lane order profile, then the status is changedto “non-compliant” and “active”—unless the POs on the load had all beenpreviously been set with a “non-compliance” prior to load creation andmarked as grouped, and there were no changes to all of the orders on thenewly created load. (cases, wgt, cube, ship via, ship from, etc). Inthis instance, still record a generation for the order(s) and also setthe previous reason code(s).

If the PO has been imported and assigned to a load, and the load isassigned to an unintended subroute, then set to “non-compliant” and“active”. Represent the same options of available (intended andalternative) subroutes, with the unintended subroute visible. Forcalculating the mismatching details, use the lane order profile from thelinked intended subroute to the available unintended subroute. Use theMissing Subroute indicator. Highlight the subroute in red. If the PO hasbeen imported and assigned to a load, and the load is NOT assigned to asubroute, then set to “non-compliant” and “active”. For the loads thathave an “active” “non-compliant code”, then they will get included backto the widget.

FIG. 24 illustrates the alert widget. The alert widget presents all ofthe purchase orders that need to be actioned by a user. Based on theCompliance Alert setup, the alert is presented to the load planner, theAS buyer, and/or the ops support set on the dist vend, as well as theaccount manager set on the Partner record. In the instance the buyer,planner, ops support, and/or account manager is the same user, then onlyshow one alert. The total count of the open alerts is grouped byPartner, and totaled on the alerts. The user can select a totaled valueand drill down to the Order Compliance Screen, with the repopulatedstatuses. For Active Compliant, set the statuses of active andcompliant, Set the user (buyer, planner, ops, accnt mang), and Set thepartner. For Active non-compliant, Set the statuses of active and noncompliant, Set the user (buyer, planner, ops, accnt mang), and Set thepartner.

Users stop or close an alert by accomplishing one of the following: 1) Auser sets a “non-compliance” code or an “ignore” through import screenprocess, or 2) POs status has changed to “compliant” and/or “closed”.

Any time a purchase order changes, then the same validation processpreferably occurs to make sure the change still falls within tolerance.Any “non-compliance” or “ignore PO” code that has been previously set ispreferably presented back onto the alert details and widget. Once a POis set to delivered status, then the alert is preferably not shownagain.

FIG. 25 illustrates a non-compliance report. As described above, once anon-compliant purchase order has been processed, the compliancereporting processor may receive the actual logistics information andcompare it to the previous desired solution in a non-compliance report.As shown in FIG. 25, the non-compliance report shows the total purchaseorders non-compliance, the percentage of POs that were non-compliance,the target and actual margins, and the margin delta both in dollars andin percentages.

Thus, one or more embodiments of the present inventor provide, for thedesired ordering and routing plan for a given lane or set of lanes,financial targets are stored that indicate the expected Freightallowance (“revenue”) and carrier expense per shipment. Expense targetsare set for line haul, fuel, and specific accessorial charges expected,based on a primary carrier. With targets captured for a specificordering and routing plan, and captured for both revenue and cost, thesystem can measure and report on the variance from financial target forevery shipment, and the dollar impact of non-compliance with the rulesthat are set for a particular planned order and routing solution to beviable.

For the desired ordering and routing plan for a given lane or set oflanes, order rules are stored that indicate the minimum and/or maximumthresholds for weight, cube, pallet count, freight allowance, transitduration (days), and pickup and delivery windows. These thresholds setthe operational boundaries within which an order and routing solutionwill be viable and will meet margin expectations. With the storage ofthese rules, non-compliance can be highlighted for any order placed onthe lane, and alerts triggered that enable corrective action prior totender.

Compliance with the order rules is determined and reported as orders arecreated and refined, and prior to the creation and tender of a freightshipment to a carrier. By locating the compliance monitoring andalerting prior to shipment tender, buyers are given the opportunity tocorrect the size and timing of purchase orders, so as to pro-activelyprotect against the loss of freight margin when possible.

Alerts of non-compliance provide visibility into the specific rulesviolated by the order, and depict the revenue, cost, and margin dollarimpact on the given shipment. By leveraging the financial targets andorder rules to demonstrate the nature and impact of buyernon-compliance, the user is able to undertake a specific correctiveaction. Measurement of financial impact also allows load planners andbuyers to focus attention on the non-compliance issues with the highestimpact on overall freight margin.

Alerts of non-compliance are able to show the compliance of a PO withmultiple solutions (“subroutes”). Lanes can have multiple alternateorder and routing plans, to cover expected variations in order timingand size. By depicting the compliance and performance against financialtargets for all related solutions simultaneously, the system enables aplanning-based approach to freight margin optimization even in dynamicreplenishment environments, and prevents users from having to eliminatemore complex solutions because they are too unpredictable.

Non-compliance of purchase orders with order rules, and correspondingfinancial impact, are reported in summary after shipment execution, toprovide measurement of total non-compliance impact, and providedirection for broader corrective action.

While particular elements, embodiments, and applications of the presentinvention have been shown and described, it is understood that theinvention is not limited thereto because modifications may be made bythose skilled in the art, particularly in light of the foregoingteaching. It is therefore contemplated by the appended claims to coversuch modifications and incorporate those features which come within thespirit and scope of the invention.

1. A method for electronically monitoring purchasing activity, saidmethod including: electronically receiving master data including pickupdata, dropoff data, and carrier rate data for at least one product;electronically receiving an optimized order plan based on said masterdata, wherein said optimized order plan includes at least one lane orderrule for purchasing said at least one product, wherein said at least onelane order rule is determined by minimizing the cost of transportingsaid at least one product; electronically receiving a purchase order forsaid at least one product from a purchase order originator;electronically comparing said purchase order to said at least one laneorder rule; and when said purchase order does not comply with said atleast one lane order rule, electronically transmitting a computerizednotification of said non-compliance to said purchase order originator.2. The method of claim 1 wherein said at least one lane order ruleincludes a requirement for order timing size and frequency.
 3. Themethod of claim 2 wherein said optimized order plan has been optimizedto lower overall cost of transporting products using said at least onelane order rule.
 4. The method of claim 1 wherein said master datafurther includes freight allowance and carrier expense per shipment. 5.The method of claim 4 wherein said at least one lane order rule isdetermined using said freight allowance and said carrier expense pershipment.
 6. The method of claim 1 wherein said notification ofnon-compliance provides said purchase order originator with the optionto override said notification of non-compliance.
 7. The method of claim6 further including calculating a shipping cost associated with saidnon-compliant purchase order.
 8. The method of claim 7 wherein saidnotification of non-compliance displays said shipping cost associatedwith said non-compliant purchase order.
 9. The method of claim 8 whereinsaid notification of non-compliance displays the difference between saidshipping cost associated with said non-compliant purchase order andanticipated shipping cost associated with said lane order rule.
 10. Themethod of claim 9 further including calculating a shipping costassociated with a proposed alternative purchase order
 11. The method ofclaim 10 wherein said notification of non-compliance displays saidshipping cost associated with said non-compliant purchase order, saidshipping cost associated with said proposed alternative purchase order,and anticipated shipping cost associated with said lane order rule. 12.The method of claim 1 wherein said purchase order originator maysubsequently modify said purchase order to form a modified purchaseorder.
 13. The method of claim 12 further including: electronicallycomparing said modified purchase order to said at least one lane orderrule; and when said modified purchase order does not comply with said atleast one lane order rule, electronically transmitting a computerizednotification of said non-compliance to said modified purchase orderoriginator.
 14. A method for electronically reporting compliance with anoptimized order plan; said method including: electronically receivingmaster data including pickup data, dropoff data, and carrier rate datafor at least one product; electronically receiving an optimized orderplan based on said master data, wherein said optimized order planincludes at least one lane order rule for purchasing said at least oneproduct, wherein said at least one lane order rule is determined byminimizing the cost of transporting said at least one product;electronically receiving actual shipment data with regard to an actualshipment of said at least one product including an actual shipment cost;and electronically transmitting a computerized compliance report listingsaid at least one lane order rule, a cost of transporting said at leastone product in compliance with said at least one lane order rule, saidactual shipment of said at least one product, said actual shipment cost.15. The method of claim 14 wherein said computerized compliance reportlisting shows the difference between actual shipment cost and cost oftransporting said at least one product in compliance with said at leastone lane order rule.
 16. The method of claim 14 wherein saidcomputerized compliance report listing summarizes for a plurality ofshipments a cost of transporting said plurality of shipments incompliance with said at least one lane order rule and said actualshipment cost of said plurality of shipments.
 17. The method of claim 16wherein said computerized compliance report listing shows the differencebetween said actual shipment cost for said plurality of shipments andsaid cost of transporting said plurality of shipments in compliance withsaid at least one lane order rule.
 18. The method of claim 14 whereinsaid computerized compliance report listing summarizes for a pluralityof products a cost of transporting said plurality of products incompliance with said at least one lane order rule and said actualshipment cost of said plurality of products.
 19. The method of claim 18wherein said computerized compliance report listing shows the differencebetween said actual shipment cost for said plurality of products andsaid cost of transporting said plurality of products in compliance withsaid at least one lane order rule.
 20. A system for electronicallymonitoring purchasing activity, said system including: a master datacomputerized database storing master data including pickup data, dropoffdata, and carrier rate data for at least one product; an order plancomputerized database storing an optimized order plan based on saidmaster data, wherein said optimized order plan includes at least onelane order rule for purchasing said at least one product, wherein saidat least one lane order rule is determined by minimizing the cost oftransporting said at least one product; and a computerized systemelectronically receiving a purchase order for said at least one productfrom a purchase order originator, wherein said computerized systemretrieves said at least one lane order rule from said order plancomputerized database and compares said purchase order to said at leastone lane order rule, wherein, when said purchase order does not complywith said at least one lane order rule, said computerized systemelectronically transmits a computerized notification of saidnon-compliance to said purchase order originator.